A multi-spectral and hyperspectral image dataset for evaluating chemical traits and the water status of avocado, olive and grape through leaf dehydration under laboratory conditions
Abstract Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of vegetation in laboratory settings, and also hold the potential of assessing vegetation of large portions of land. H...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-85714-8 |
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| author | Juan Sebastian Estrada Rodrigo Demarco Ciarán Miceal Johnson Matias Zañartu Andres Fuentes Fernando Auat Cheein |
| author_facet | Juan Sebastian Estrada Rodrigo Demarco Ciarán Miceal Johnson Matias Zañartu Andres Fuentes Fernando Auat Cheein |
| author_sort | Juan Sebastian Estrada |
| collection | DOAJ |
| description | Abstract Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of vegetation in laboratory settings, and also hold the potential of assessing vegetation of large portions of land. However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and grape trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation. |
| format | Article |
| id | doaj-art-3167c11a5cd549c28a87fca60546d8ea |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3167c11a5cd549c28a87fca60546d8ea2025-01-26T12:27:29ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-025-85714-8A multi-spectral and hyperspectral image dataset for evaluating chemical traits and the water status of avocado, olive and grape through leaf dehydration under laboratory conditionsJuan Sebastian Estrada0Rodrigo Demarco1Ciarán Miceal Johnson2Matias Zañartu3Andres Fuentes4Fernando Auat Cheein5Department of Electronic Engineering, Universidad Tecnica Federico Santa MariaDepartment of Industrial Engineering, Universidad Tecnica Federico Santa MariaUK National Robotarium, School of Engineering and Physical Sciences, Heriot-Watt UniversityDepartment of Electronic Engineering, Universidad Tecnica Federico Santa MariaDepartment of Industrial Engineering, Universidad Tecnica Federico Santa MariaDepartment of Electronic Engineering, Universidad Tecnica Federico Santa MariaAbstract Assessing the health status of vegetation is of vital importance for all stakeholders. Multi-spectral and hyper-spectral imaging systems are tools for evaluating the health of vegetation in laboratory settings, and also hold the potential of assessing vegetation of large portions of land. However, the literature lacks benchmark datasets to test algorithms for predicting plant health status, with most researchers creating tailored datasets. This work presents a dataset composed of multi-spectral images, hyper-spectral reflectance values, and measurements of weight, chlorophyll, and nitrogen content of leaves at five different drying stages, from avocado, olive, and grape trees, which are common crops in the Valparaíso region of Chile. This dataset is a valuable asset for developing tools in the field of precision agriculture and assessing the general health status of vegetation.https://doi.org/10.1038/s41598-025-85714-8 |
| spellingShingle | Juan Sebastian Estrada Rodrigo Demarco Ciarán Miceal Johnson Matias Zañartu Andres Fuentes Fernando Auat Cheein A multi-spectral and hyperspectral image dataset for evaluating chemical traits and the water status of avocado, olive and grape through leaf dehydration under laboratory conditions Scientific Reports |
| title | A multi-spectral and hyperspectral image dataset for evaluating chemical traits and the water status of avocado, olive and grape through leaf dehydration under laboratory conditions |
| title_full | A multi-spectral and hyperspectral image dataset for evaluating chemical traits and the water status of avocado, olive and grape through leaf dehydration under laboratory conditions |
| title_fullStr | A multi-spectral and hyperspectral image dataset for evaluating chemical traits and the water status of avocado, olive and grape through leaf dehydration under laboratory conditions |
| title_full_unstemmed | A multi-spectral and hyperspectral image dataset for evaluating chemical traits and the water status of avocado, olive and grape through leaf dehydration under laboratory conditions |
| title_short | A multi-spectral and hyperspectral image dataset for evaluating chemical traits and the water status of avocado, olive and grape through leaf dehydration under laboratory conditions |
| title_sort | multi spectral and hyperspectral image dataset for evaluating chemical traits and the water status of avocado olive and grape through leaf dehydration under laboratory conditions |
| url | https://doi.org/10.1038/s41598-025-85714-8 |
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